The present invention relates to segmentation of objects in medical images. More specifically it relates to the segmentation of the esophagus using a Bayesian approach.
Catheter ablation of left atrium (LA) has become standard treatment method for atrial fibrillation, the leading cause of stroke. In recent years, there were several reports of occurrence of atrio-esophageal fistula, a complication of ablating in the posterior wall of the left atrium that usually results in death. Pre-op CT could provide valuable information about the location of esophagus relative to the left atrium that could be used to devise ablation strategy with reduced risk of atrio-esophageal fistula. In the situation where patient is not under general anesthesia and esophagus could move during the ablation procedure, the extracted esophagus shape and its position along anterior/posterior direction could still help to aid the ablation decision of the doctor, arguably less accurate. This limitation could be reduced by applying the methodology of extracting the esophagus directly on interventional CT, because the data is then acquired during the procedure and hence less prone to the problem of esophagus movement.
The segmentation of the esophagus with “standard” techniques is not possible because it is only visible in a few slices. However, a specialist is still able to guess its position with a good confidence. This is possible because he does not rely only on visible parts of the esophagus but also on the context, i.e. the surrounding structures. As shown in FIG. 1, the respective locations of the esophagus (with 101 and 102 as its extreme points), the left atrium and the aorta are well constrained. Including this high-level knowledge can improve dramatically the robustness and reliability of the segmentation. Accordingly improved methods for segmenting the esophagus in medical images are required.